Fertilization rate is an independent predictor of implantation rate  

Oleh:

Rosen, Mitchell P. ; Shehua, Shen ; Rinaudo, Paolo F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Fertility and Sterility (keterangan: ada di ClinicalKey) vol. 94 no. 04 (Sep. 2010), page 1328-1333.
Topik: Fertilization rate; IVF; predictors; pregnancy; implantation rate; ART; oocyte quality; ICSI

Ketersediaan

Perpustakaan FK Nomor Panggil: F02.K.2010.05 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Objective To determine whether fertilization rate serves as a biological assay, reflects oocyte quality, and may be used to help predict patient implantation rate. Design Retrospective cohort study. Setting Academic center. Patient(s) Couples undergoing 3603 in vitro fertilization (IVF) cycles from 2001 to 2007. Intervention(s) None. Main Outcome Measure(s) We compared the implantation rate among cycles with high versus low fertilization rate. Univariate analyses were performed to determine the association of implantation rate with potential confounding variables: age, day-3 follicle-stimulating hormone level, day-3 estradiol level, antral follicle count, oocyte number, cycle attempts, embryo grading, and number of embryos transferred. Multivariate analysis was then performed to determine whether the fertilization rate remained an independent predictor. Result(s) Cutoffs for fertilization rate were 50% for intracytoplasmic sperm injection (ICSI) and 75% for conventional insemination. Higher ICSI fertilization was statistically significantly associated with the implantation rate (25.2% vs. 17.8 %). After adjusting for variables associated with implantation rate, fertilization rate for ICSI remained a strong independent predictor of implantation. Higher conventional insemination fertilization was statistically significantly associated with implantation (32.1% vs. 25.7%) and remained a statistically significant predictor after adjustment. Conclusion(s) Fertilization is a strong, independent predictor of implantation rate and may be useful in modeling to guide decision making for the number of embryos to transfer.

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